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<p>The deployment of 5G networks has incorporated advanced multiple access technologies like sparse code multiple access (SCMA) to address growing demands for high-speed connectivity and massive device access. As a Non-Orthogonal Multiple Access technique, SCMA enables multiple users to share identical time-frequency resources through sparse codebook-based multiplexing. Nevertheless, achieving efficient scheduling in SCMA networks remains challenging due to the inherent complexities in dynamic resource allocation. This paper proposed two artificial intelligence-based approaches for resource scheduling in 5G SCMA networks: a multi-agent deep reinforcement learning (MARL)-based approach and a large language model (LLM)-empowered methodology. We systematically investigate these AI techniques to develop adaptive resource scheduling policies capable of responding to diverse network conditions. Simulation results validate that the proposed MARL-based and LLM-based schedulers not only effectively learn optimal scheduling strategies but also outperform conventional algorithms, particularly in terms of system throughput and user fairness metrics.</p>

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Scheduling and resource allocation in 5G SCMA cellular networks using AI-based methods

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

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  • Mincheng Zhao,
  • Mingqing Han,
  • Jiyuan Pan,
  • Ligang Du,
  • Yongfeng Yan,
  • Fuqiang Li

摘要

The deployment of 5G networks has incorporated advanced multiple access technologies like sparse code multiple access (SCMA) to address growing demands for high-speed connectivity and massive device access. As a Non-Orthogonal Multiple Access technique, SCMA enables multiple users to share identical time-frequency resources through sparse codebook-based multiplexing. Nevertheless, achieving efficient scheduling in SCMA networks remains challenging due to the inherent complexities in dynamic resource allocation. This paper proposed two artificial intelligence-based approaches for resource scheduling in 5G SCMA networks: a multi-agent deep reinforcement learning (MARL)-based approach and a large language model (LLM)-empowered methodology. We systematically investigate these AI techniques to develop adaptive resource scheduling policies capable of responding to diverse network conditions. Simulation results validate that the proposed MARL-based and LLM-based schedulers not only effectively learn optimal scheduling strategies but also outperform conventional algorithms, particularly in terms of system throughput and user fairness metrics.